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--- |
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library_name: transformers |
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license: mit |
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base_model: gpt2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- hatexplain |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: gpt2-hatexplain |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: hatexplain |
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type: hatexplain |
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config: plain_text |
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split: validation |
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args: plain_text |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6917879417879418 |
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- name: Precision |
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type: precision |
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value: 0.6837783251259969 |
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- name: Recall |
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type: recall |
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value: 0.6917879417879418 |
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- name: F1 |
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type: f1 |
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value: 0.6822435740647693 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# gpt2-hatexplain |
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the hatexplain dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7507 |
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- Accuracy: 0.6918 |
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- Precision: 0.6838 |
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- Recall: 0.6918 |
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- F1: 0.6822 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.7396 | 1.0 | 962 | 0.7567 | 0.6790 | 0.6713 | 0.6790 | 0.6641 | |
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| 0.6697 | 2.0 | 1924 | 0.7486 | 0.6842 | 0.6769 | 0.6842 | 0.6783 | |
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| 0.7573 | 3.0 | 2886 | 0.7685 | 0.6748 | 0.6658 | 0.6748 | 0.6656 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu118 |
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- Datasets 3.1.0 |
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- Tokenizers 0.21.0 |
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